{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# this is our global size of label text  on the plots\n",
    "matplotlib.rc('xtick', labelsize=20) \n",
    "matplotlib.rc('ytick', labelsize=20) \n",
    "\n",
    "# This line ensures that the plot is displayed\n",
    "# inside the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8693, 14), (4277, 13))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('../input/train.csv')\n",
    "test = pd.read_csv('../input/test.csv')\n",
    "\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck             Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0  Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0     Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0    Altark Susent   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "HomePlanet      201\n",
       "CryoSleep       217\n",
       "Cabin           199\n",
       "Destination     182\n",
       "Age             179\n",
       "VIP             203\n",
       "RoomService     181\n",
       "FoodCourt       183\n",
       "ShoppingMall    208\n",
       "Spa             183\n",
       "VRDeck          188\n",
       "Name            200\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8693, 17) (4277, 16)\n",
      "(8693, 15) (4277, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Transported</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Num</th>\n",
       "      <th>Side</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>True</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Destination   Age    VIP  RoomService  \\\n",
       "0     0001_01     Europa     False  TRAPPIST-1e  39.0  False          0.0   \n",
       "1     0002_01      Earth     False  TRAPPIST-1e  24.0  False        109.0   \n",
       "\n",
       "   FoodCourt  ShoppingMall    Spa  VRDeck  Transported Deck Num Side  \n",
       "0        0.0           0.0    0.0     0.0        False    B   0    P  \n",
       "1        9.0          25.0  549.0    44.0         True    F   0    S  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Deck','Num','Side']] = train['Cabin'].str.split('/', expand=True)\n",
    "test[['Deck','Num','Side']] = test['Cabin'].str.split('/', expand=True)\n",
    "print(train.shape, test.shape)\n",
    "train = train.drop(columns=['Cabin', 'Name'])\n",
    "test = test.drop(columns=['Cabin', 'Name'])\n",
    "print(train.shape, test.shape)\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in train.columns:\n",
    "    if train[i].dtypes in ['object','bool']:\n",
    "        train[i].fillna(train[i].mode()[0],inplace=True)\n",
    "    else :\n",
    "        train[i].fillna(train[i].mean(),inplace=True)\n",
    "\n",
    "train['Num'] = train['Num'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in test.columns:\n",
    "    if test[i].dtypes in ['object','bool']:\n",
    "        test[i].fillna(test[i].mode()[0],inplace=True)\n",
    "    else :\n",
    "        test[i].fillna(test[i].mean(),inplace=True)\n",
    "\n",
    "test['Num'] = test['Num'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PassengerId     0\n",
       " HomePlanet      0\n",
       " CryoSleep       0\n",
       " Destination     0\n",
       " Age             0\n",
       " VIP             0\n",
       " RoomService     0\n",
       " FoodCourt       0\n",
       " ShoppingMall    0\n",
       " Spa             0\n",
       " VRDeck          0\n",
       " Transported     0\n",
       " Deck            0\n",
       " Num             0\n",
       " Side            0\n",
       " dtype: int64,\n",
       " PassengerId     0\n",
       " HomePlanet      0\n",
       " CryoSleep       0\n",
       " Destination     0\n",
       " Age             0\n",
       " VIP             0\n",
       " RoomService     0\n",
       " FoodCourt       0\n",
       " ShoppingMall    0\n",
       " Spa             0\n",
       " VRDeck          0\n",
       " Deck            0\n",
       " Num             0\n",
       " Side            0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum(), test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axis=plt.subplots(7,2,figsize=(15,20))\n",
    "features = [f for f in train.columns if f not in ('PassengerId')]\n",
    "\n",
    "for i in features:\n",
    "    x_ind = features.index(i) // 2\n",
    "    y_ind = features.index(i) % 2\n",
    "    temp_df = train[[i]]\n",
    "    temp_df_sum = pd.DataFrame(temp_df[i].value_counts()).reset_index()\n",
    "    # print(temp_df_sum)\n",
    "    if train[i].dtypes in ['object','bool']:\n",
    "        axis[x_ind,y_ind].bar(temp_df_sum['index'], temp_df_sum[i])\n",
    "        axis[x_ind,y_ind].set_xticks(temp_df_sum['index'])\n",
    "    else :\n",
    "        axis[x_ind,y_ind].hist(temp_df[i])\n",
    "    axis[x_ind,y_ind].set_title(i)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7937, 15)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 清理异常值\n",
    "from scipy.stats import zscore\n",
    "\n",
    "temp_df = pd.DataFrame()\n",
    "\n",
    "# 找到数值类型的特征\n",
    "for f in features:\n",
    "    if train[f].dtype not in ('object', 'bool'):\n",
    "        temp_df[f] = train[f]\n",
    "\n",
    "outlier_df = zscore(temp_df)\n",
    "outlier_df['abs_zscore'] = outlier_df.abs().max(axis=1)\n",
    "train =  train[train.index.isin(outlier_df[outlier_df['abs_zscore']<=3].index)]\n",
    "train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('../input/train_clear.csv')\n",
    "test.to_csv('../input/test_clear.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
